Which of the following is NOT a step in the AI development lifecycle?

The concept of the AI development lifecycle consists of a series of critical steps that guide the creation of an artificial intelligence model. Each step plays a unique role in ensuring that the final product is accurate and effective.

Data preprocessing is an essential step in the lifecycle that involves cleaning and preparing the data for analysis. This step often addresses issues such as removing anomalies, handling missing values, and transforming the data into a suitable format for modeling.

Model selection is another crucial phase where different algorithms or techniques are evaluated to find the most appropriate model for the given data and task. This step is fundamental because it directly impacts the performance of the AI system, as various models could yield different results.

Training involves taking the selected model and using the prepared data to enable the model to learn from it. This is an iterative process where the model's parameters are adjusted to minimize errors and improve accuracy, making it a key aspect of building any AI system.

Feature extraction, while mentioned in the response, is not typically categorized as a standalone step in the same way as data preprocessing, model selection, or training. Instead, feature extraction is often considered part of the broader data preprocessing step, where relevant attributes or quantities are derived from the raw data to facilitate the learning process.

Thus, the